geobr: Download Official Spatial Data Sets of Brazil

geobr is a computational package to download official spatial data sets of Brazil. The package includes a wide range of geospatial data in geopackage format (like shapefiles but better), available at various geographic scales and for various years with harmonized attributes, projection and topology (see detailed list of available data sets below).

The package is currently available in R and Python.

R	Python	Repo

Installation R

# From CRAN
install.packages("geobr")
library(geobr)

# or use the development version with latest features
utils::remove.packages('geobr')
remotes::install_github("ipeaGIT/geobr", subdir = "r-package")
library(geobr)

obs. If you use Linux, you need to install a couple dependencies before installing the libraries sf and geobr. More info here.

Installation Python

pip install geobr

Windows users:

conda create -n geo_env
conda activate geo_env  
conda config --env --add channels conda-forge  
conda config --env --set channel_priority strict  
conda install python=3 geopandas  
pip install geobr

Basic Usage

The syntax of all geobr functions operate on the same logic so it becomes intuitive to download any data set using a single line of code. Like this:

R, reading the data as an sf object

library(geobr)

# Read specific municipality at a given year
mun <- read_municipality(code_muni=1200179, year=2017)

# Read all municipalities of given state at a given year
mun <- read_municipality(code_muni=33, year=2010) # or
mun <- read_municipality(code_muni="RJ", year=2010)

# Read all municipalities in the country at a given year
mun <- read_municipality(code_muni="all", year=2018)

More examples in the intro Vignette

Python, reading the data as a geopandas object

from geobr import read_municipality

# Read specific municipality at a given year
mun = read_municipality(code_muni=1200179, year=2017)

# Read all municipalities of given state at a given year
mun = read_municipality(code_muni=33, year=2010) # or
mun = read_municipality(code_muni="RJ", year=2010)

# Read all municipalities in the country at a given year
mun = read_municipality(code_muni="all", year=2018)

More examples here

Available datasets:

👉 All datasets use geodetic reference system "SIRGAS2000", CRS(4674).

Function	Geographies available	Years available	Source
read_country	Country	1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020	IBGE
read_region	Region	2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020	IBGE
read_state	States	1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020	IBGE
read_meso_region	Meso region	2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020	IBGE
read_micro_region	Micro region	2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020	IBGE
read_intermediate_region	Intermediate region	2017, 2019, 2020	IBGE
read_immediate_region	Immediate region	2017, 2019, 2020	IBGE
read_municipality	Municipality	1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2005, 2007, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022	IBGE
read_municipal_seat	Municipality seats (sedes municipais)	1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2010	IBGE
read_weighting_area	Census weighting area (área de ponderação)	2010	IBGE
read_census_tract	Census tract (setor censitário)	2000, 2010, 2017, 2019, 2020	IBGE
read_statistical_grid	Statistical Grid of 200 x 200 meters	2010	IBGE
read_metro_area	Metropolitan areas	1970, 2001, 2002, 2003, 2005, 2010, 2013, 2014, 2015, 2016, 2017, 2018	IBGE
read_urban_area	Urban footprints	2005, 2015	IBGE
read_amazon	Brazil's Legal Amazon	2012	MMA
read_biomes	Biomes	2004, 2019	IBGE
read_conservation_units	Environmental Conservation Units	201909	MMA
read_disaster_risk_area	Disaster risk areas	2010	CEMADEN and IBGE
read_indigenous_land	Indigenous lands	201907, 202103	FUNAI
read_semiarid	Semi Arid region	2005, 2017, 2021, 2022	IBGE
read_health_facilities	Health facilities	201505, 202303	CNES, DataSUS
read_health_region	Health regions and macro regions	1991, 1994, 1997, 2001, 2005, 2013	DataSUS
read_neighborhood	Neighborhood limits	2010	IBGE
read_schools	Schools	2020, 2023	INEP
read_comparable_areas	Historically comparable municipalities, aka Áreas mínimas comparáveis (AMCs)	1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2010	IBGE
read_urban_concentrations	Urban concentration areas (concentrações urbanas)	2015	IBGE
read_pop_arrangements	Population arrangements (arranjos populacionais)	2015	IBGE

Other functions:

Function	Action
list_geobr	List all datasets available in the geobr package
lookup_muni	Look up municipality codes by their name, or the other way around
grid_state_correspondence_table	Loads a correspondence table indicating what quadrants of IBGE's statistical grid intersect with each state
cep_to_state	Determine the state of a given CEP postal code
...	...

Note 1. Data sets and Functions marked with "dev" are only available in the development version of geobr.

Note 2. Most data sets are available at scale 1:250,000 (see documentation for details).

Coming soon:

Geography	Years available	Source
read_census_tract	2007	IBGE
Longitudinal Database* of micro regions	various years	IBGE
Longitudinal Database* of Census tracts	various years	IBGE
...	...	...

'*' Longitudinal Database refers to áreas mínimas comparáveis (AMCs)

• Quadro geográfico de referência para produção, análise e disseminação de estatísticas
• Outros arquivos e recortes estão disponiveis em ftp://geoftp.ibge.gov.br/.

Contributing to geobr

If you would like to contribute to geobr and add new functions or data sets, please check this guide to propose your contribution.

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Related projects

As of today, there is another R package with similar functionalities: simplefeaturesbr. The geobr package has a few advantages when compared to simplefeaturesbr, including for example:

• A same syntax structure across all functions, making the package very easy and intuitive to use
• Access to a wider range of official spatial data sets, such as states and municipalities, but also macro-, meso- and micro-regions, weighting areas, census tracts, urbanized areas, etc
• Access to shapefiles with updated geometries for various years
• Harmonized attributes and geographic projections across geographies and years
• Option to download geometries with simplified borders for fast rendering
• Stable version published on CRAN for R users, and on PyPI for Python users

Similar packages for other countries/continents

• Africa: afrimapr
• Argentina: geoAr
• Brazil: geobr
• Canada: cancensus
• Chile: chilemapas
• Czech Republic: RCzechia
• Finland: geofi
• Kazakhstan: (geokz)[https://github.com/arodionoff/geokz]
• Peru: mapsPERU
• Spain: mapSpain
• UK: geographr
• Uruguay: geouy
• USA: tigris
• Global (political administrative boundaries): rgeoboundaries

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Credits

Original shapefiles are created by official government institutions. The geobr package is developed by a team at the Institute for Applied Economic Research (Ipea), Brazil. If you want to cite this package, you can cite it as:

• Pereira, R.H.M.; Gonçalves, C.N.; et. all (2019) geobr: Loads Shapefiles of Official Spatial Data Sets of Brazil. GitHub repository - https://github.com/ipeaGIT/geobr.